Modelling the Social Buffering Hypothesis in an Artificial Life Environment

Khan, Imran; Lewis, Matthew; Cañamero, Lola

doi:10.1162/isal_a_00302

Cited by 4 publications

(24 citation statements)

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“…To summarise, we found a significant relationship between the (type of) physiological adaptation experienced by socially-bonded agents (the different types of "social buffering" mechanisms), their subsequent interactions with the social and physical environment, and the ability for unbonded agents to adapt to the (social and physical) environment; underpinned by (positive) affective contagion which played a causal role in improving the wellbeing of agents without social support. Though there may be additional behavioural dynamics to consider than those discussed, our findings nevertheless emphasise the importance of investigating micro (and meso-)level interactions between agents and the environments to understand emergent social dynamics; a point we have also made previously (Khan et al, 2020).…”

Section: Stress-regulation "Social Buffering" and Affective Contagionmentioning

confidence: 55%

“…Screenshots of these worlds are seen in Figure 5. Social Bond Combinations: In line with our previous study (Khan et al, 2020), we test three different combinations of agents that share an affective social bond (Fig. 4, i.e.…”

Section: Experiments and Resultsmentioning

confidence: 97%

“…Building on our previous work (Khan et al, 2020), our agent model comprises three main components: (1) a homeostatically-controlled Action-Selection Architecture (ASA), which selects behaviours to regulate two internal homeostatically-controlled variables; (2) the Social Assessment Component (SAC), which adapts agent behaviour based on appraisals of affective social relationships with others, and (3) an affective hormonal system which modulates certain aspects of agent "physiology" and behaviour.…”

Section: Agent Modelmentioning

confidence: 99%

“…where Bond (Ai) is a boolean flag denoting whether a social bond exists between agents A and i (1 if bond exists, else 0), DSI Ai is the measure of affective quality of the social bond (between 0-2), and OT is a hormone (oxytocin) which we describe in the next section. As this investigation is focused on agents without affective social bonds, we summarise the SAC here, and refer to (Khan et al, 2020) for more detail.…”

Section: Social Assessment Componentmentioning

confidence: 99%

“…However, while previous research into these sociallyadaptive mechanisms (including "social buffering" effects) have generally reported performance improvements for (artificial) agents that have been endowed with them, less attention has been given to the potential effects on the wider society, and in particular on agents in those social groups who lack mechanisms of (social) adaptation. The work presented in this paper is motivated by this limitation, and builds on our initial investigation into the "social buffering" phenomenon in societies of artificial agents (Khan et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Adaptation-By-Proxy: Contagion Effect of Social Buffering in an Artificial Society

Khan¹,

Cañamero²

2021

The 2021 Conference on Artificial Life

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The "social buffering" phenomenon proposes that social support facilitates wellbeing by reducing stress in a number of different ways. While this phenomenon may benefit agents with social support from others, its potential effects on the wider social group are less clear. Using a biologicallyinspired artificial life model, we have investigated how some of the hypothesised hormonal mechanisms that underpin the "social buffering" phenomenon affect the wellbeing and interactions of agents without social support across numerous social and physical contexts. We tested these effects in a small, rank-based society, with half of the agents endowed with numerous hormonal mechanisms associated with "social buffering", and half without. Surprisingly, our results found that these "social buffering" mechanisms provided survivalrelated advantages to agents without social support across numerous conditions. We found that agents with sociallyadaptive mechanisms themselves become a proxy for adaptation, and suggest that, in some (artificial) societies, "social buffering" may be a contagious phenomenon.

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Section: Stress-regulation "Social Buffering" and Affective Contagionmentioning

confidence: 55%

Section: Experiments and Resultsmentioning

confidence: 97%

Section: Agent Modelmentioning

confidence: 99%

Section: Social Assessment Componentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Adaptation-By-Proxy: Contagion Effect of Social Buffering in an Artificial Society

Khan¹,

Cañamero²

2021

The 2021 Conference on Artificial Life

Self Cite

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Modeling Neuroendocrine Autonomic Responses in Embodied Autonomous Robots

Maroto-Gómez

Castro‐González

Malfáz

et al. 2022

Advanced Intelligent Systems

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The Importance of Noise Colour in Simulations of Evolutionary Systems

et al. 2021

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Simulations of evolutionary dynamics often employ white noise as a model of stochastic environmental variation. Whilst white noise has the advantages of being simply generated and analytically tractable, empirical analyses demonstrate that most real environmental time series have power spectral densities consistent with pink or red noise, in which lower frequencies contribute proportionally greater amplitudes than higher frequencies. Simulated white noise environments may therefore fail to capture key components of real environmental time series, leading to erroneous results. To explore the effects of different noise colours on evolving populations, a simple evolutionary model of the interaction between life-history and the specialism-generalism axis was developed. Simulations were conducted using a range of noise colours as the environments to which agents adapted. Results demonstrate complex interactions between noise colour, reproductive rate, and the degree of evolved generalism; importantly, contradictory conclusions arise from simulations using white as opposed to red noise, suggesting that noise colour plays a fundamental role in generating adaptive responses. These results are discussed in the context of previous research on evolutionary responses to fluctuating environments, and it is suggested that Artificial Life as a field should embrace a wider spectrum of coloured noise models to ensure that results are truly representative of environmental and evolutionary dynamics.

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Modelling the Social Buffering Hypothesis in an Artificial Life Environment

Cited by 4 publications

References 0 publications

Adaptation-By-Proxy: Contagion Effect of Social Buffering in an Artificial Society

Adaptation-By-Proxy: Contagion Effect of Social Buffering in an Artificial Society

Modeling Neuroendocrine Autonomic Responses in Embodied Autonomous Robots

The Importance of Noise Colour in Simulations of Evolutionary Systems

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